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A novel algorithm for the construction of fast English sentence retrieval model using a combination of ontology and advanced machine learning techniques

  • Application of soft computing
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Abstract

These days, exploring information retrieval models is one of the most essential aspects of English sentence retrieval research. These models are driven by diverse retrieval mechanisms that offer varying similarity calculations and directly influence the final result ranking. However, despite decades of work due to technical constraints, deep semantic analysis has been challenging. This gap emphasizes the importance of a precise semantic understanding of information acquisition through learning approaches. Based on the above opening, this paper establishes a fast retrieval model of English sentences based on the statistical language model (SLM). First, the proposed method utilizes SLM to extract significant feature words from the corpus. These feature words are identified by analyzing co-occurrence patterns and frequency distributions within the standard. Second, it employs the N-gram model to calculate the probabilities of word occurrences based on their contextual dependencies. This framework represents feature words and their associated probabilities in a structured manner by capturing the intricate nuances of language semantics. Third, the model integrates ontology to bridge the gap between human language and machine understanding by enabling the mapping natural language expressions to conceptual entities. Finally, the suggested model retrieves English sentences through semantic matching by leveraging the comprehensive semantic framework and ontology-based search. The experimental study revealed that the proposed model demonstrated an impressive retrieval ratio of 98.5% by outperforming existing models in the comparison. Moreover, these results show that the proposed algorithm performs better than the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and the accuracy of this algorithm is improved by 7.52% compared with TF-IDF. When the labelled corpus is very small and the unlabeled corpus is relatively large, the algorithm enhances the classifier’s performance by 12.6%. This shows that the algorithm used in this paper reduces the influence of the synonym processing stage on the overall performance while retaining the advantages of high precision and accuracy of calculation results.

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  1. Xinyang Vocational and Technical College, Xinyang, 464000, Henan, China

    Haibo Wang

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  1. Haibo Wang
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Correspondence to Haibo Wang.

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Wang, H. A novel algorithm for the construction of fast English sentence retrieval model using a combination of ontology and advanced machine learning techniques. Soft Comput 27, 18129–18146 (2023). https://doi.org/10.1007/s00500-023-09224-3

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